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96年12月7日(五) 2:00 ~3:00 P.M.
演講者姓名： Dr. Boris Choy
演講者服務單位： Department of Mathematical Sciences, University of Technology of Sydney, Australia
Robust Bayesian analysis of loss reserves data using the generalized-t distribution
Abstract
This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavy-tailed distributions including the Student-t and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the log-ANOVA, log-ANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC).
Keywords: Bayesian approach, state space model, threshold model, scale mixtures of uniform distribution, deviance information criterion.
